The present disclosure relates to a system and method for archiving and analyzing big data at rest or in transit. In particular, the present disclosure relates to a system and method for archiving event data and process data, and providing analytic results generated from the archived data.
Data is generated by processes. Entities such as manufacturers, business enterprises, financial systems, biological systems, physical systems, smart homes, smart cities, etc. implement complex business processes that continuously generate massive amount of data in real-time. The generated data reflects the properties of the corresponding processes, and thus, analysis of such data is important for process optimization and process mining.
Existing solutions for archiving big data often store event data coming in from data sources in archive storage before any analysis of the data is performed. This approach generally requires a significant amount of time to analyze the data to perform data analytics, and therefore is unable to provide analytic results in a timely manner when there is latency in getting the data stored to the archive storage.